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Minimum Time Ship Maneuvering using Neural Network and Nonlinear Model Predictive Compensator

使用神经网络和非线性模型预测补偿器的最短时间船舶操纵

基本信息

批准号：
15560692
负责人：
MIZUNO Naoki
金额：
$ 1.86万
依托单位：
Nagoya Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

项目摘要

In this research, a new minimum-time ship maneuvering system is developed.The proposed system is mainly composed of two parts. The one is a neural network based optimal solution generator and another is a nonlinear model predictive compensator. The neural network generates the optimal solution for real situation by interpolating pre-computed minimum-time solutions for typical control conditions. The optimal solutions for the various minimum time maneuvering are numerically computed based on the sophisticated nonlinear dynamical model of the ship (MMG model) and are learned off-line by the neural network for interpolation. Moreover, the same nonlinear dynamical model, which is used for the computation of the optimal solutions, is used to simulate the ship's future course on-line. Based on the computed ship's future course, the predictive control error caused by some disturbances is compensated by modifying the control input for minimum time maneuvering.First, the solving technique of the minimum-time maneuvering problems and the mathematical model of the ship's dynamics are briefly reviewed. Next, the minimum-time parallel deviation maneuvering problem and its solutions are introduced as an example of the feasible study realized by proposed system. Then, a minimum-time maneuvering system with neural network and nonlinear model predictive compensator is introduced. Finally, computer simulations and on-line experiments are carried out for a training ship Shioji Maru (425 gross tonnage).This research presented a new practical ship's minimum-time maneuvering system with neural network and nonlinear model predictive compensator. In the minimum-time deviation problems, the system gives approximate solutions in a short computing time and good tracking performance in real situations. Moreover, the actual sea trials demonstrate the effectiveness of the proposed system.

本研究开发了一种新型的最短时间船舶操纵系统。该系统主要由两部分组成。一个是基于神经网络的最优解生成器，另一个是非线性模型预测补偿器。神经网络通过对典型控制条件的预先计算的最短时间解进行插值来生成实际情况的最优解。基于复杂的船舶非线性动力学模型（MMG模型）对各种最短时间操纵的最优解进行数值计算，并通过神经网络离线学习进行插值。此外，用于计算最优解的相同非线性动力学模型也用于在线模拟船舶的未来航向。基于计算出的船舶未来航向，通过修正最小时间操纵的控制输入来补偿由一些扰动引起的预测控制误差。首先，简要回顾了最短时间操纵问题的求解技术和船舶动力学的数学模型。接下来，介绍了最小时间并行偏差机动问题及其解决方案，作为该系统实现的可行性研究的例子。然后，介绍了一种带有神经网络和非线性模型预测补偿器的最短时间机动系统。最后，对盐路丸号训练舰（425总吨）进行了计算机模拟和在线实验。本研究提出了一种新型实用船舶最小时间操纵系统，该系统采用神经网络和非线性模型预测补偿器。在最小时间偏差问题上，系统能够在较短的计算时间内给出近似解，并且在实际情况下具有良好的跟踪性能。此外，实际的海上试验证明了所提出系统的有效性。